How Should You Verify AI Facts, Inferences, Advice, and Decisions?

Classify AI output before checking it: trace facts to evidence, test inferences, compare recommendations, and keep final decisions with a named person.

One AI answer often mixes facts, inferences, recommendations, and decisions. They cannot be checked with one method. Verify facts against evidence, test inferences for assumptions and alternatives, evaluate recommendations against goals and costs, and assign decisions to a person authorized to accept the consequences.

Suppose AI reviews event feedback and writes: “Sixty percent of participants wanted a longer session, so the next event should run for three hours and the Q&A should be removed.”

This looks like one conclusion, but it contains four layers. The feedback count is a fact. “The event was too short” is an inference. Extending the session is a recommendation. Removing the Q&A is a decision that someone still has to make. Confirming the 60% does not validate the rest.

Classify each statement before choosing the evidence. This prevents one real number from lending false authority to an entire plan.

NIST materials support context-based evaluation, human oversight, and explicit responsibility. The four categories and checks below are my beginner framework, not an official NIST classification.

Facts: where can this be directly confirmed?

Facts can be checked against data, documents, records, or direct observations: dates, prices, response counts, quotations, and events that occurred.

Find the original source rather than relying on an AI summary.
Check the date, version, population, and unit.
Reproduce calculations from the original values.
Mark unsupported statements as unverified instead of asking AI to validate itself.

In the event example, first check how many people responded, how the question was phrased, and whether “longer session” was an available response. If only five of ten participants responded and three selected “longer,” then “60% of participants” incorrectly treats respondents as the whole group.

NIST’s Generative AI Profile recommends reviewing and verifying sources and citations. NIST: Generative Artificial Intelligence Profile Source verification handles the factual layer; it does not prove that the interpretation or action is sound.

Inferences: is this the only explanation consistent with the facts?

An inference interprets known information: “They want a longer session because there was too much content,” or “Traffic fell because readers dislike the headline.” It may be reasonable, but it is not directly written in the source.

Test it in three steps:

List the facts being used.
State the assumptions connecting those facts to the conclusion.
Identify at least one alternative explanation.

A preference for a longer event might mean the content was overloaded. It might also mean the discussion was valuable, the break was too short, or the survey forced an artificial choice. “Time may have been insufficient” can remain a hypothesis, but it should not be presented as a known cause.

The current data confirms ______.
The AI infers ______, which assumes ______.
Another possible explanation is ______.
We would need ______ to distinguish them.

Recommendations: under what conditions is this worth doing?

A recommendation is not simply true or false. It proposes an action under particular goals and constraints. A three-hour event might help, but it also increases venue cost, attendance burden, and speaker effort.

Add four fields:

What goal is the recommendation meant to improve?
Which unverified assumptions does it depend on?
What costs, side effects, or alternatives does it displace?
Is there a smaller, reversible test?

Instead of immediately adding an hour, the team could keep the two-hour limit and replace twenty minutes with small-group discussion, then compare the next round of feedback. Small tests are not always superior, but when evidence is limited they can reduce commitment while creating new information.

Decisions: who has authority to accept the result?

A decision converts a recommendation into a commitment: confirming a date, spending money, removing Q&A, or notifying participants. AI can organize options and identify missing information. Fluent writing does not give it decision authority.

The NIST AI RMF Core calls for clear roles, responsibilities, and human oversight in human-AI configurations. NIST: AI RMF Core In everyday work, the closer an action is to money, external commitments, employment, law, safety, or irreversible effects, the more important it is to name the approver.

Decision:
Available options:
Confirmed facts:
Disputed inferences:
Main cost of each option:
Person authorized to decide:
Post-decision check or recovery path:

“The AI recommended it” cannot fill the authorized-person field.

One sentence may need four rows

Type	Content	Appropriate check	Current treatment
Fact	60% of participants wanted more time	Check responses, question, and denominator	Rewrite as “60% of respondents,” or recalculate
Inference	Therefore, the event was too short	Look for alternatives and missing evidence	Mark as a hypothesis
Recommendation	Run the next event for three hours	Compare goals, costs, and smaller tests	Design a lower-cost test first
Decision	Remove the Q&A	Name the agenda owner	Let the responsible person decide and record why

The table is useful because each layer carries only the weight its evidence allows.

Practice: separate and test a mixed answer

> This month the website received 800 views, compared with 1,000 last month. Therefore, readers dislike the new homepage. We should immediately restore the old version and stop all current content updates.

Four-part verification record

Fact:
Evidence and review result:

Inference:
Assumptions and alternative explanations:

Recommendation:
Goal, required conditions, cost, and smaller test:

Decision:
Authorized person and required approval:

Final treatment: use / revise / pause / reject
Reason:

Only the two monthly view counts are directly stated, and even those require checks for the measurement period, tracking health, and traffic sources. “Readers dislike the homepage” is an inference. Restoring the old version is a recommendation. Stopping all content updates is a decision.

By separating them, you do not have to accept or reject the whole answer. You can retain a verified fact, pause an inference, narrow a recommendation, and return the final decision to the responsible person.

The next lesson applies this responsibility boundary before data enters an AI system: who controls the account, which data may be used, what can be shared, and which safer substitute should be used when the answer is unclear.